This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Developing Data Allocation Schemes by Incremental Mining of User Moving Patterns in a Mobile Computing System
January/February 2003 (vol. 15 no. 1)
pp. 70-85

Abstract—In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two data allocation schemes, which explore different levels of mining results, are devised: one utilizes the set level of moving patterns and the other utilizes the path level of moving patterns. As can be seen later, the former is useful for the allocation of read-intensive data objects, whereas the latter is good for the allocation of update-intensive data objects. The data allocation schemes for shared data, which are able to achieve local optimization and global optimization, are also developed. Performance of these data allocation schemes is comparatively analyzed. It is shown by our simulation results that the knowledge obtained from the user moving patterns is very important in devising effective data allocation schemes which can lead to significant performance improvement in a mobile computing system.

[1] R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proc. 1993 ACM-SIGMOD Int'l Conf. Management of Data, pp. 207-216, May 1993.
[2] R. Agrawal and J.C. Shafer, Parallel Mining of Association Rules: Design, Implementation, and Experience IEEE Trans. Knowledge and Data Eng., pp. 487-499, Dec. 1996.
[3] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proc. 1995 Int'l Conf. Data Eng., pp. 3-14, Mar. 1995.
[4] B. Bruegge and B. Bennington, “Applications of Mobile Computing and Communication,” IEEE Personal Comm., pp. 64-71, Feb. 1996.
[5] M.-S. Chen, J. Han, and P.S. Yu, Data Mining: An Overview from Database Perspective IEEE Trans. Knowledge and Data Eng., vol. 8, no. 6, pp. 866-883, Dec. 1996.
[6] M.-S. Chen, J.-S. Park, and P.S. Yu, “Efficient Data Mining for Path Traversal Patterns,” IEEE Trans. Knowledge and Data Eng., vol. 10, no. 2, pp. 209-221, Apr. 1998.
[7] D.W. Cheung, V.T. Ng, W. Fu, and Y. Fu, “Efficient Mining Association Rules in Distributed Databases,” IEEE Trans. Knowledge and Data Eng., vol. 8, no. 6, pp. 911-922, Dec. 1996.
[8] T.H. Cormen,C.E. Leiserson, and R.L. Rivest,Introduction to Algorithms.Cambridge, Mass.: MIT Press/McGraw-Hill, 1990.
[9] N. Davies, G.S. Blair, K. Cheverst, and A. Friday, “Supporting Collaborative Application in a Heterogeneous Mobile Environment,” Computer Comm. Specical Issues on Mobile Computing, 1996.
[10] M.H. Dunham, A. Helal, and S. Balakrishnan, “A Mobile Transaction Model That Captures Both the Data and Movement Behavior,” ACM J. Mobile Networks and Applications, vol. 2, pp. 149-162, 1997.
[11] M.H. Dunham and V. Kumar, “Location Dependent Data and its Management in Mobile Databases,” Proc. Ninth Int'l Workshop Database and Expert Systems Applications, pp. 26-29, Aug. 1998.
[12] EIA/TIA. Cellular Radio Telecommunication Intersystem Operations, 1991.
[13] A. Elmagarmid, J. Jain, and T. Furukawa, “Wireless Client/Server Computing for Personal Information Services and Applications,” ACM SIGMOD RECORD, vol. 24, no. 4, pp. 16-21, Dec. 1995.
[14] J. Han, G. Dong, and Y. Yin, Efficient Mining of Partial Periodic Patterns in Time Series Database Proc. 15th Int'l Conf. Data Eng., pp. 106-115, Mar. 1999.
[15] T. Imielinski and B.R. Badrinath, “Querying in Highly Mobile and Distributed Environment,” Proc. 18th Int'l Conf. Vary Large Data Bases, pp. 41-52, Aug. 1992.
[16] T. Imielinski and B.R. Badrinath, “Wireless Computing: Challenges in Data Management,” Comm. ACM, vol. 37, no. 10, Oct. 1994.
[17] J. Jannink, D. Lam, N. Shivakumar, J. Widom, and D. Cox, “Efficient amd Flexible Location Management Techniques for Wireless Communication Systems,” ACM J. Wireless Networks, vol. 3, no. 5, pp. 361-374, 1997.
[18] D.N. Knisely, S. Kumar, S. Laha, and S. Nanda, “Evolution of Wireless Data Services: IS-95 to cdma2000,” IEEE Comm. Magazine, pp. 140-149, Oct. 1998.
[19] N. Krishnakumar and R. Jain, “Escrow Techniques for Mobile Sales and Inventory Applications,” ACM J. Wireless Network, vol. 3, no. 3, pp. 235-246, July 1997.
[20] D. Lam, D.C. Cox, and J. Widom, “Teletrafic Modeling for Personal Communication Services,” IEEE Comm., vo. 35, no. 2, pp. 79-87, Feb. 1997.
[21] J.-L. Lin and M.H. Dunham, Mining Association Rules: Anti-Skew Algorithms Proc. Int'l Conf. Data Eng., pp. 486-493, 1998.
[22] Y.-B. Lin, “GSM Network Signaling,” ACM Mobile Computing and Comm., vol. 1, no. 2, pp. 11-16, 1997.
[23] Y.-B. Lin, “Modeling Techniques for Large-Scale PCS Networks,” IEEE Comm. Magazine, vol. 35, no. 2, pp. 102-107, Feb. 1997.
[24] Y.-B. Lin, “Reducing Location Update Cost in a PCS Network,” IEEE/ACM Trans. Networking, vol. 5, no. 1, pp. 25-33, June 1997.
[25] R.T. Ng and J. Han, "Efficient and Effective Clustering Methods for Spatial Data Mining," Proc. 20th Int'l Conf. Very Large Databases, Morgan Kaufmann, 1994, pp. 144-155.
[26] S.K. Palat and S. Andresen, “Comparison of Replication of the User Mobility Profile with Caching for Reduction of HLR Accesses,” 1997 IEEE Int'l Conf. Personal Wireless Comm., pp. 173-177, 1997.
[27] J.-S. Park, M.-S. Chen, and P.S. Yu, Using a Hash-Based Method with Transaction Trimming for Mining Association Rules IEEE Trans. Knowledge and Data Eng., vol. 9, no. 5, pp. 813-825, Oct. 1997.
[28] W.-C. Peng and M.-S. Chen, “A Dynamic and Adaptive Cache Retrieval Scheme for Mobile Computing Systems,” Proc. Third IFCIS Conf. Cooperative Information Systems (CoopIS '98), pp. 251-258, Aug. 1998.
[29] M. Satyanarayanan, “Mobile Information Access,” IEEE Personal Comm., pp. 26-33, Feb. 1996.
[30] J. Shafer, R. Agrawal, and M. Mehta, “SPRINT: A Scalable Parallel Classifier for Data Mining,” Proc. 22th Int'l Conf. Very Large Databases, Sept. 1996.
[31] N. Shivakumar and J. Jannink, J. Widom, “Per-User Profile Replication in Mobile Environments: Algorithms, Analysis and Simulation Results,” ACM J. Mobile Networks and Applications, vol. 2, pp. 129-140, 1997.
[32] N.R. Sollenberger, N. Seshadri, and R. Cox, “The Evolution of IS-136 TDMA for Third-Generation Wireless Services,” IEEE Personal Comm., pp. 8-18, June 1999.
[33] WAP Forum.http:/www.wapforum.org/, 2002.
[34] O. Wolfson, S. Jajodia, and Y. Huang, “An Adaptive Data Replication Algorithm,” ACM Trans. Database Systems, vol. 22, no. 4, pp. 255-314, June 1997.

Index Terms:
Data mining, mobile computing, user moving patterns, data allocation scheme, mobile database.
Citation:
Wen-Chih Peng, Ming-Syan Chen, "Developing Data Allocation Schemes by Incremental Mining of User Moving Patterns in a Mobile Computing System," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 1, pp. 70-85, Jan.-Feb. 2003, doi:10.1109/TKDE.2003.1161583
Usage of this product signifies your acceptance of the Terms of Use.